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A fusion method of data association and virtual detection for minimizing track loss and false track

Title
A fusion method of data association and virtual detection for minimizing track loss and false track
Authors
Lim, Young-ChulLee, Chung-HeeKwon, SoonLee, Jong-hun
DGIST Authors
Lim, Young-Chul; Lee, Chung-HeeKwon, SoonLee, Jong-hun
Issue Date
2010
Citation
2010 IEEE Intelligent Vehicles Symposium, IV 2010, 301-306
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
Abstract
In this paper, we present a method to track multiple moving vehicles using the global nearest neighborhood (GNN) data association (DA) based on 2D global position and virtual detection based on motion tracking. Unlikely the single target tracking, multiple target tracking needs to associate observation-to-track pairs. DA is a process to determine which measurements are used to update each track. We use the GNN data association not to lost track and not to connect incorrect measurements. GNN is a simple, robust, and optimal technique for intelligent vehicle applications with a stereo vision system that can reliably estimates the position of a vehicle. However, an incomplete detection and recognition technique bring low track maintenance due to missed detections and false alarms. A complementary virtual detection method adds to GNN method. Virtual detection is used to recover the missed detection by motion tracking when the track maintains for some periods. Motion tracking estimates virtual region of interest (ROI) of the missed detection using a pyramidal Lukas-Kanade feature tracker. Next, GNN associates the lost tracks and virtual measurements if the measurement exists in the validation gate. Our experimental results show that our tracking method works well in a stereo vision system with incomplete detection and recognition ability. ©2010 IEEE.
URI
http://hdl.handle.net/20.500.11750/3942
DOI
10.1109/IVS.2010.5548084
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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